Overview

Dataset statistics

Number of variables11
Number of observations17521
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory88.0 B

Variable types

Numeric9
Categorical2

Alerts

Velocidad_Prom is highly overall correlated with humedad and 1 other fieldsHigh correlation
hora is highly overall correlated with radiacion_binHigh correlation
humedad is highly overall correlated with Velocidad_Prom and 3 other fieldsHigh correlation
precipitacion is highly overall correlated with precipitacion_binHigh correlation
precipitacion_bin is highly overall correlated with precipitacionHigh correlation
radiacion is highly overall correlated with humedad and 2 other fieldsHigh correlation
radiacion_bin is highly overall correlated with hora and 3 other fieldsHigh correlation
temperatura is highly overall correlated with Velocidad_Prom and 3 other fieldsHigh correlation
semana_año has 336 (1.9%) zerosZeros
hora has 731 (4.2%) zerosZeros
precipitacion has 14449 (82.5%) zerosZeros
radiacion has 8360 (47.7%) zerosZeros

Reproduction

Analysis started2024-06-14 16:58:59.437612
Analysis finished2024-06-14 16:59:09.608101
Duration10.17 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

semana_año
Real number (ℝ)

ZEROS 

Distinct52
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50046611
Minimum0
Maximum1
Zeros336
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size137.0 KiB
2024-06-14T11:59:09.728035image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.039215686
Q10.25490196
median0.50980392
Q30.74509804
95-th percentile0.96078431
Maximum1
Range1
Interquartile range (IQR)0.49019608

Descriptive statistics

Standard deviation0.29400938
Coefficient of variation (CV)0.58747111
Kurtosis-1.1987737
Mean0.50046611
Median Absolute Deviation (MAD)0.25490196
Skewness-0.0042470573
Sum8768.6667
Variance0.086441515
MonotonicityNot monotonic
2024-06-14T11:59:09.872344image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6666666667 385
 
2.2%
0.6862745098 336
 
1.9%
0.1960784314 336
 
1.9%
0.2156862745 336
 
1.9%
0.2352941176 336
 
1.9%
0.2549019608 336
 
1.9%
0.2745098039 336
 
1.9%
0.2941176471 336
 
1.9%
0.3137254902 336
 
1.9%
0.3333333333 336
 
1.9%
Other values (42) 14112
80.5%
ValueCountFrequency (%)
0 336
1.9%
0.01960784314 336
1.9%
0.03921568627 336
1.9%
0.05882352941 336
1.9%
0.07843137255 336
1.9%
0.09803921569 336
1.9%
0.1176470588 336
1.9%
0.137254902 336
1.9%
0.1568627451 336
1.9%
0.1764705882 336
1.9%
ValueCountFrequency (%)
1 336
1.9%
0.9803921569 336
1.9%
0.9607843137 336
1.9%
0.9411764706 336
1.9%
0.9215686275 336
1.9%
0.9019607843 336
1.9%
0.8823529412 336
1.9%
0.862745098 336
1.9%
0.8431372549 336
1.9%
0.8235294118 336
1.9%

hora
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49997146
Minimum0
Maximum1
Zeros731
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size137.0 KiB
2024-06-14T11:59:10.008155image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.043478261
Q10.2173913
median0.47826087
Q30.73913043
95-th percentile0.95652174
Maximum1
Range1
Interquartile range (IQR)0.52173913

Descriptive statistics

Standard deviation0.30098834
Coefficient of variation (CV)0.60201103
Kurtosis-1.2042036
Mean0.49997146
Median Absolute Deviation (MAD)0.26086957
Skewness2.2783665 × 10-5
Sum8760
Variance0.090593979
MonotonicityNot monotonic
2024-06-14T11:59:10.123133image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 731
 
4.2%
0.04347826087 730
 
4.2%
0.9565217391 730
 
4.2%
0.9130434783 730
 
4.2%
0.8695652174 730
 
4.2%
0.8260869565 730
 
4.2%
0.7826086957 730
 
4.2%
0.7391304348 730
 
4.2%
0.6956521739 730
 
4.2%
0.652173913 730
 
4.2%
Other values (14) 10220
58.3%
ValueCountFrequency (%)
0 731
4.2%
0.04347826087 730
4.2%
0.08695652174 730
4.2%
0.1304347826 730
4.2%
0.1739130435 730
4.2%
0.2173913043 730
4.2%
0.2608695652 730
4.2%
0.3043478261 730
4.2%
0.347826087 730
4.2%
0.3913043478 730
4.2%
ValueCountFrequency (%)
1 730
4.2%
0.9565217391 730
4.2%
0.9130434783 730
4.2%
0.8695652174 730
4.2%
0.8260869565 730
4.2%
0.7826086957 730
4.2%
0.7391304348 730
4.2%
0.6956521739 730
4.2%
0.652173913 730
4.2%
0.6086956522 730
4.2%

humedad
Real number (ℝ)

HIGH CORRELATION 

Distinct12164
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57215534
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size137.0 KiB
2024-06-14T11:59:10.254640image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.12820511
Q10.30635005
median0.63385493
Q30.82761512
95-th percentile0.92014618
Maximum1
Range1
Interquartile range (IQR)0.52126507

Descriptive statistics

Standard deviation0.27663534
Coefficient of variation (CV)0.48349692
Kurtosis-1.3039877
Mean0.57215534
Median Absolute Deviation (MAD)0.23122241
Skewness-0.33044917
Sum10024.734
Variance0.076527114
MonotonicityNot monotonic
2024-06-14T11:59:10.372200image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8424538657 58
 
0.3%
0.5386253708 16
 
0.1%
0.5989232674 10
 
0.1%
0.8801826762 10
 
0.1%
0.4725341873 8
 
< 0.1%
0.8946739478 7
 
< 0.1%
0.882457123 7
 
< 0.1%
0.8908089893 7
 
< 0.1%
0.8592733394 7
 
< 0.1%
0.8416828636 6
 
< 0.1%
Other values (12154) 17385
99.2%
ValueCountFrequency (%)
0 1
< 0.1%
0.002037203667 1
< 0.1%
0.002899155166 1
< 0.1%
0.002989186404 1
< 0.1%
0.003263353601 1
< 0.1%
0.003405132041 1
< 0.1%
0.003415283073 1
< 0.1%
0.003664985097 1
< 0.1%
0.004717471864 1
< 0.1%
0.004783532542 1
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.994152467 1
< 0.1%
0.9912203579 1
< 0.1%
0.9904461588 1
< 0.1%
0.9889998149 1
< 0.1%
0.987823589 1
< 0.1%
0.987151875 1
< 0.1%
0.9867489916 1
< 0.1%
0.9858429009 1
< 0.1%
0.9856751667 1
< 0.1%

precipitacion
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct216
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10814109
Minimum0
Maximum1
Zeros14449
Zeros (%)82.5%
Negative0
Negative (%)0.0%
Memory size137.0 KiB
2024-06-14T11:59:10.639698image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.98701246
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.27812074
Coefficient of variation (CV)2.5718322
Kurtosis4.8211784
Mean0.10814109
Median Absolute Deviation (MAD)0
Skewness2.5281203
Sum1894.7401
Variance0.077351149
MonotonicityNot monotonic
2024-06-14T11:59:10.771474image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14449
82.5%
0.1409005531 749
 
4.3%
0.2618362113 246
 
1.4%
0.3656517293 155
 
0.9%
0.5313221131 112
 
0.6%
0.4547842673 98
 
0.6%
0.5970548975 91
 
0.5%
0.6535165423 72
 
0.4%
0.7020219984 59
 
0.3%
1 58
 
0.3%
Other values (206) 1432
 
8.2%
ValueCountFrequency (%)
0 14449
82.5%
0.1409005531 749
 
4.3%
0.2618362113 246
 
1.4%
0.3656517293 155
 
0.9%
0.4547842673 98
 
0.6%
0.5313221131 112
 
0.6%
0.5970548975 91
 
0.5%
0.6535165423 72
 
0.4%
0.7020219984 59
 
0.3%
0.743698677 53
 
0.3%
ValueCountFrequency (%)
1 58
0.3%
1 7
 
< 0.1%
1 1
 
< 0.1%
1 2
 
< 0.1%
1 1
 
< 0.1%
1 1
 
< 0.1%
1 2
 
< 0.1%
1 1
 
< 0.1%
1 1
 
< 0.1%
1 1
 
< 0.1%

presion
Real number (ℝ)

Distinct4580
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4776589
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size137.0 KiB
2024-06-14T11:59:10.903621image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.22706705
Q10.37402222
median0.48313903
Q30.58210108
95-th percentile0.71492526
Maximum1
Range1
Interquartile range (IQR)0.20807886

Descriptive statistics

Standard deviation0.1474849
Coefficient of variation (CV)0.30876614
Kurtosis-0.38501086
Mean0.4776589
Median Absolute Deviation (MAD)0.10351754
Skewness-0.068266097
Sum8369.0616
Variance0.021751795
MonotonicityNot monotonic
2024-06-14T11:59:11.024694image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6254514174 63
 
0.4%
0.4489950169 17
 
0.1%
0.6357187784 16
 
0.1%
0.4685329797 16
 
0.1%
0.4245028873 16
 
0.1%
0.5278595184 15
 
0.1%
0.4896962892 15
 
0.1%
0.4967402147 15
 
0.1%
0.476067105 14
 
0.1%
0.39407001 14
 
0.1%
Other values (4570) 17320
98.9%
ValueCountFrequency (%)
0 1
< 0.1%
0.01779231184 1
< 0.1%
0.0391644156 1
< 0.1%
0.04167321803 1
< 0.1%
0.04683794288 1
< 0.1%
0.06025210662 1
< 0.1%
0.06149772981 1
< 0.1%
0.0654406366 1
< 0.1%
0.07290519297 1
< 0.1%
0.07469628149 1
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.9646554892 1
< 0.1%
0.9599971924 1
< 0.1%
0.9504754548 1
< 0.1%
0.9485765373 1
< 0.1%
0.9465609162 1
< 0.1%
0.9303915573 1
< 0.1%
0.9302740165 1
< 0.1%
0.9241715193 1
< 0.1%
0.9191394627 1
< 0.1%

temperatura
Real number (ℝ)

HIGH CORRELATION 

Distinct6547
Distinct (%)37.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.60003846
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size137.0 KiB
2024-06-14T11:59:11.140553image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.35065672
Q10.46442546
median0.58539967
Q30.74446508
95-th percentile0.86758509
Maximum1
Range1
Interquartile range (IQR)0.28003961

Descriptive statistics

Standard deviation0.16999715
Coefficient of variation (CV)0.28331043
Kurtosis-0.80967576
Mean0.60003846
Median Absolute Deviation (MAD)0.13738459
Skewness0.049085112
Sum10513.274
Variance0.028899033
MonotonicityNot monotonic
2024-06-14T11:59:11.270683image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5221517552 65
 
0.4%
0.6335639667 16
 
0.1%
0.6099557675 16
 
0.1%
0.6405865569 12
 
0.1%
0.4393581157 12
 
0.1%
0.5140452413 11
 
0.1%
0.4681789839 11
 
0.1%
0.410791514 11
 
0.1%
0.5149670536 11
 
0.1%
0.3860723363 10
 
0.1%
Other values (6537) 17346
99.0%
ValueCountFrequency (%)
0 1
< 0.1%
0.0005663970862 1
< 0.1%
0.0007079403044 1
< 0.1%
0.01181945578 1
< 0.1%
0.0157246154 1
< 0.1%
0.0233456494 1
< 0.1%
0.02637572387 1
< 0.1%
0.07490791888 1
< 0.1%
0.07879909784 1
< 0.1%
0.0790578786 1
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.9962395408 1
< 0.1%
0.9889801752 1
< 0.1%
0.9886454115 1
< 0.1%
0.988593881 1
< 0.1%
0.9877683586 1
< 0.1%
0.9869667745 1
< 0.1%
0.9846291702 1
< 0.1%
0.9844467036 1
< 0.1%
0.9832191173 1
< 0.1%

Velocidad_Prom
Real number (ℝ)

HIGH CORRELATION 

Distinct2549
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57891305
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size137.0 KiB
2024-06-14T11:59:11.404120image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.29139164
Q10.42211291
median0.54843326
Q30.76508206
95-th percentile0.86997035
Maximum1
Range1
Interquartile range (IQR)0.34296914

Descriptive statistics

Standard deviation0.19306784
Coefficient of variation (CV)0.33350059
Kurtosis-1.1274455
Mean0.57891305
Median Absolute Deviation (MAD)0.16112294
Skewness0.054677648
Sum10143.135
Variance0.037275193
MonotonicityNot monotonic
2024-06-14T11:59:11.525505image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4447278906 83
 
0.5%
0.3980164129 44
 
0.3%
0.3635004953 43
 
0.2%
0.4206663132 41
 
0.2%
0.372241666 41
 
0.2%
0.3432845401 41
 
0.2%
0.463142827 41
 
0.2%
0.4148034822 39
 
0.2%
0.3856950164 39
 
0.2%
0.3575220578 38
 
0.2%
Other values (2539) 17071
97.4%
ValueCountFrequency (%)
0 1
< 0.1%
0.02493482022 1
< 0.1%
0.02664971544 1
< 0.1%
0.03005437882 1
< 0.1%
0.03509966155 1
< 0.1%
0.04980888892 1
< 0.1%
0.05927734689 1
< 0.1%
0.06544737942 1
< 0.1%
0.06697263538 2
< 0.1%
0.07000283541 1
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.976582506 1
< 0.1%
0.9682632263 1
< 0.1%
0.9653012807 1
< 0.1%
0.9639981503 1
< 0.1%
0.9596746249 1
< 0.1%
0.9581051781 1
< 0.1%
0.9558393439 1
< 0.1%
0.9546957656 1
< 0.1%
0.9543742741 1
< 0.1%

radiacion
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9095
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.41404346
Minimum0
Maximum1
Zeros8360
Zeros (%)47.7%
Negative0
Negative (%)0.0%
Memory size137.0 KiB
2024-06-14T11:59:11.641434image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.45060176
Q30.84869468
95-th percentile0.95926127
Maximum1
Range1
Interquartile range (IQR)0.84869468

Descriptive statistics

Standard deviation0.41279895
Coefficient of variation (CV)0.99699427
Kurtosis-1.8222495
Mean0.41404346
Median Absolute Deviation (MAD)0.45060176
Skewness0.10807511
Sum7254.4554
Variance0.17040298
MonotonicityNot monotonic
2024-06-14T11:59:11.762418image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8360
47.7%
0.8010464425 16
 
0.1%
0.4022379077 2
 
< 0.1%
0.8607484583 2
 
< 0.1%
0.6052750816 2
 
< 0.1%
0.6379223394 2
 
< 0.1%
0.6768258095 2
 
< 0.1%
0.4546239675 2
 
< 0.1%
0.4622267428 2
 
< 0.1%
0.958321837 2
 
< 0.1%
Other values (9085) 9129
52.1%
ValueCountFrequency (%)
0 8360
47.7%
0.0001748792942 1
 
< 0.1%
0.000872544911 1
 
< 0.1%
0.0355663343 1
 
< 0.1%
0.05189278975 1
 
< 0.1%
0.09507164871 1
 
< 0.1%
0.09747880986 1
 
< 0.1%
0.1377290499 1
 
< 0.1%
0.1446891766 1
 
< 0.1%
0.145219828 1
 
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.9998598983 1
< 0.1%
0.9994086737 1
< 0.1%
0.9988695266 1
< 0.1%
0.9976330331 1
< 0.1%
0.9971646585 1
< 0.1%
0.9971339147 1
< 0.1%
0.9965775187 1
< 0.1%
0.9963467212 1
< 0.1%
0.9958379246 1
< 0.1%

humedad_suelo
Real number (ℝ)

Distinct6022
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50251529
Minimum0
Maximum1
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size137.0 KiB
2024-06-14T11:59:11.889567image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.20799849
Q10.40909521
median0.51054174
Q30.59472098
95-th percentile0.76845987
Maximum1
Range1
Interquartile range (IQR)0.18562578

Descriptive statistics

Standard deviation0.1588805
Coefficient of variation (CV)0.31617048
Kurtosis0.77233689
Mean0.50251529
Median Absolute Deviation (MAD)0.091519334
Skewness-0.015142089
Sum8804.5704
Variance0.025243013
MonotonicityNot monotonic
2024-06-14T11:59:12.022246image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5105417415 936
 
5.3%
0.463269358 252
 
1.4%
0.4443604047 241
 
1.4%
0.3214522076 240
 
1.4%
0.6239954619 223
 
1.3%
0.3592701144 204
 
1.2%
0.4254514513 195
 
1.1%
0.340361161 186
 
1.1%
0.4916327881 176
 
1.0%
0.557814125 163
 
0.9%
Other values (6012) 14705
83.9%
ValueCountFrequency (%)
0 4
 
< 0.1%
0.0006618133686 1
 
< 0.1%
0.002836343008 1
 
< 0.1%
0.003498156377 2
 
< 0.1%
0.006618133686 1
 
< 0.1%
0.009265387161 1
 
< 0.1%
0.01106173773 1
 
< 0.1%
0.01767987142 1
 
< 0.1%
0.01862531909 1
 
< 0.1%
0.01890895339 15
0.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
0.9993381866 1
 
< 0.1%
0.9988654628 1
 
< 0.1%
0.9984872837 1
 
< 0.1%
0.9982036494 1
 
< 0.1%
0.9977309256 1
 
< 0.1%
0.9972582018 3
< 0.1%
0.9966909332 1
 
< 0.1%
0.9965018436 1
 
< 0.1%
0.9960291198 1
 
< 0.1%

precipitacion_bin
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size137.0 KiB
0.0
14449 
1.0
3072 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters52563
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 14449
82.5%
1.0 3072
 
17.5%

Length

2024-06-14T11:59:12.126643image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-14T11:59:12.242563image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14449
82.5%
1.0 3072
 
17.5%

Most occurring characters

ValueCountFrequency (%)
0 31970
60.8%
. 17521
33.3%
1 3072
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 52563
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 31970
60.8%
. 17521
33.3%
1 3072
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 52563
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 31970
60.8%
. 17521
33.3%
1 3072
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 52563
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 31970
60.8%
. 17521
33.3%
1 3072
 
5.8%

radiacion_bin
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size137.0 KiB
1.0
9161 
0.0
8360 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters52563
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 9161
52.3%
0.0 8360
47.7%

Length

2024-06-14T11:59:12.342565image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-14T11:59:12.439177image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 9161
52.3%
0.0 8360
47.7%

Most occurring characters

ValueCountFrequency (%)
0 25881
49.2%
. 17521
33.3%
1 9161
 
17.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 52563
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 25881
49.2%
. 17521
33.3%
1 9161
 
17.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 52563
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 25881
49.2%
. 17521
33.3%
1 9161
 
17.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 52563
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 25881
49.2%
. 17521
33.3%
1 9161
 
17.4%

Interactions

2024-06-14T11:59:08.255255image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:00.036529image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:01.008609image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:01.976244image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:03.056455image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:04.025489image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:05.056810image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:06.135491image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:07.256881image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:08.359003image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:00.150843image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:01.121553image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:02.089631image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:03.155051image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:04.124411image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:05.161705image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:06.237777image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:07.356978image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:08.465657image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:00.254320image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:01.229558image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:02.196766image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:03.270449image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:04.245526image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:05.261345image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:06.476469image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:07.500487image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:08.581113image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:00.343899image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:01.333781image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:02.305937image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:03.370116image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:04.354616image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:05.372848image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:06.587184image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:07.607106image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:08.691413image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:00.441997image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:01.424189image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:02.405703image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:03.471712image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:04.457482image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:05.488609image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:06.691470image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:07.706545image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:08.791265image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:00.542799image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:01.551813image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:02.540304image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:03.589541image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:04.556410image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:05.604868image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:06.806849image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:07.802544image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:08.908855image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:00.643173image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:01.665073image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:02.639113image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:03.692624image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:04.673537image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:05.786947image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:06.923565image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:07.909548image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:09.021869image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:00.764116image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:01.761883image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:02.840471image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:03.808800image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:04.788467image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:05.910666image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:07.036600image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:08.025133image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:09.134357image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:00.874354image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:01.858842image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:02.951172image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:03.913013image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:04.905342image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:06.027155image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:07.154668image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-06-14T11:59:08.139951image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Correlations

2024-06-14T11:59:12.527124image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Velocidad_Promhorahumedadhumedad_sueloprecipitacionprecipitacion_binpresionradiacionradiacion_binsemana_añotemperatura
Velocidad_Prom1.0000.374-0.747-0.096-0.0530.099-0.4190.4740.427-0.0050.750
hora0.3741.000-0.314-0.0270.0370.125-0.1130.0160.867-0.0000.401
humedad-0.747-0.3141.0000.2180.2600.2520.327-0.7210.6390.047-0.890
humedad_suelo-0.096-0.0270.2181.0000.1110.1750.127-0.0260.0620.428-0.210
precipitacion-0.0530.0370.2600.1111.0001.0000.102-0.1510.1400.011-0.190
precipitacion_bin0.0990.1250.2520.1751.0001.0000.101-0.1410.0920.009-0.184
presion-0.419-0.1130.3270.1270.1020.1011.000-0.0700.3200.006-0.407
radiacion0.4740.016-0.721-0.026-0.151-0.141-0.0701.0000.9990.0060.647
radiacion_bin0.4270.8670.6390.0620.1400.0920.3200.9991.0000.0350.541
semana_año-0.005-0.0000.0470.4280.0110.0090.0060.0060.0351.0000.007
temperatura0.7500.401-0.890-0.210-0.190-0.184-0.4070.6470.5410.0071.000

Missing values

2024-06-14T11:59:09.289639image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-14T11:59:09.507064image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

semana_añohorahumedadprecipitacionpresiontemperaturaVelocidad_Promradiacionhumedad_sueloprecipitacion_binradiacion_bin
00.6666670.0000000.9912200.0000000.6634430.4231860.4517260.0000000.9787270.00.0
10.6666670.0434780.9837310.0000000.6138650.4272660.4167720.0000000.9180300.00.0
20.6666670.0869570.9627080.0000000.5837470.4318880.6090260.0000000.8698120.00.0
30.6666670.1304350.8018770.7436990.5834570.4440540.8282670.0000000.8419211.00.0
40.6666670.1739130.7516540.0000000.6113990.4284040.5957640.0000000.8064670.00.0
50.6666670.2173910.9060180.2618360.6306270.3809490.3303130.0000000.7960671.00.0
60.6666670.2608700.9625430.1409010.6492720.3734990.1355810.4962990.7752671.01.0
70.6666670.3043480.9057320.0000000.6918320.4188330.3801660.7327230.7478490.01.0
80.6666670.3478260.7853020.7436990.7560280.4873620.4384540.7338850.7511581.01.0
90.6666670.3913040.7506160.5970550.7528160.5550550.4495590.8630570.7367871.01.0
semana_añohorahumedadprecipitacionpresiontemperaturaVelocidad_Promradiacionhumedad_sueloprecipitacion_binradiacion_bin
175110.6666670.6521740.0067410.00.2961270.9718940.7620840.8954730.1406830.01.0
175120.6666670.6956520.0052500.00.2588110.9664790.7683910.7959580.1375630.01.0
175130.6666670.7391300.0314440.00.2815460.9146270.6695120.6638910.1273520.01.0
175140.6666670.7826090.0871120.00.3593380.7961440.5272330.0000000.1252720.00.0
175150.6666670.8260870.1417600.00.4188350.7423210.4700910.0000000.1229080.00.0
175160.6666670.8695650.2140320.00.5071360.7224440.5584090.0000000.1229080.00.0
175170.6666670.9130430.3521130.00.6188060.6201000.5213170.0000000.1229080.00.0
175180.6666670.9565220.4105070.00.6447420.5742220.5249490.0000000.1229080.00.0
175190.6666671.0000000.5266080.00.6272410.5293220.4334220.0000000.1229080.00.0
175200.6666670.0000000.6027030.00.6074630.4969930.3884200.0000000.1235700.00.0